3 papers
cs.LG2026
Interpretability in Deep Time Series Models Demands Semantic Alignment
Giovanni De Felice, Riccardo D'Elia, Alberto Termine +3
Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, existing interpretability approache…
cs.AI2025
Towards explainable decision support using hybrid neural models for logistic terminal automation
Riccardo D'Elia, Alberto Termine, Francesco Flammini
The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, t…
cs.LG2025
Interpretable Neural System Dynamics: Combining Deep Learning with System Dynamics Modeling to Support Critical Applications
Riccardo D'Elia
The objective of this proposal is to bridge the gap between Deep Learning (DL) and System Dynamics (SD) by developing an interpretable neural system dynamics framework. While DL ex…